Enhanced diagnostic certainty using information entropy theory

Qu, Liangsheng; Li, Liangming; Lee, Jay

doi:10.1016/j.aei.2004.08.002

Cited by 34 publications

(23 citation statements)

References 6 publications

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“…Consequently, the entropy feature is flexible and tolerant to deal with the nonlinearity problem (Engin, 2007). Several recent papers have confirmed the effectiveness of the information entropy as a quantifier applied in the fault diagnosis (Qu, Li, & Jay, 2003;Yang, Yu, & Cheng, 2006). Although the SVM theory is not new in concept (can be literated by , the application of which in machine fault diagnosis is being considered only in current years.…”

Section: Introductionmentioning

confidence: 87%

Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method

Hou-xi

Zhang

Kang

et al. 2009

Journal of Loss Prevention in the Process Industries

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Section: Introductionmentioning

confidence: 87%

Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method

Hou-xi

Zhang

Kang

et al. 2009

Journal of Loss Prevention in the Process Industries

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“…Assuming under the earthquake-induced landslide ring to the emergency, rock landslides other ris characteristics [12][13][14][15], the risk features of geological military engineering was demonstrated by Single Axis Saturated Resisting Intensity, Risk Probability, maneuverability, Maximum terra stress, and others, as shown in Table 2. Meanwhile, max and min denoted the trend of the risk characteristics of the geological landslide-threatened military engineering.…”

Section: Monitoring Parameters Geological Risk Of Militamentioning

confidence: 99%

Entropy-Based Set Pair Analysis Model on Geological Disaster Risk for Military Engineering

Quanbing¹,

Zhang²,

Wang³

2012

ENG

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Measuring the geological disaster-risked situation, is a typical non-deterministic decision-making issue in disaster pre- vention and emergency response science for military engineering. Based on the given geological disaster risk analysis mechanism, geological disaster risk monitoring matrix was established, and risk characters’ value was obtained by mining the hidden information in the monitoring matrix with Entropy theory; with Identity, Discrepancy, and Contrary of Set Pair Analysis and distance measurement, geological disaster-risked model was erected for military engineering, and the steps were given for measuring geological disaster risk, which determined geological disaster-risked SPA force and order relationship of military engineering. Finally, case showed that model has the feasibility and effectiveness over measuring the geological disaster-risked situation for military engineering

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“…With the development of the high performance and high reliability of rotating machinery, vibration fault needs to be predicted and inhibited early [1][2][3]. Therefore, how to predict and control rotor vibration faults is one of hot issues in preventing the failures of mechanical system, which leads to the advance of feasible and effective fault diagnosis methods [3][4][5][6][7][8]. However, most of present vibration analysis techniques need a mass of vibration samples to establish a fault diagnosis model to diagnose the vibration faults from a qualitative perspective.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

Fei

Bai

2014

Shock and Vibration

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To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE) and Support Vector Machine (SVM) (PPSE-SVM, for short) method) was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness) were collected under multipoint (multiple channels) and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness) in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM) for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.

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Enhanced diagnostic certainty using information entropy theory

Cited by 34 publications

References 6 publications

Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method

Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method

Entropy-Based Set Pair Analysis Model on Geological Disaster Risk for Military Engineering

Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

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